Over 2 billion people live in areas that are affected by the transmission of arthropod-borne viruses (arboviruses) such as the flaviviruses dengue (DENV) and Zika (ZIKV), and the alphavirus chikungunya (CHIKV). These arboviruses are pathogenic and cause millions of infections annually. While the surveillance is critical to understanding infection rates, controlling outbreaks, and informing patient therapeutic efforts, there is no FDA- approved method to simultaneously detect multiple arboviral infections at the point-of-care and analyze, document, and report data in real-time. In this Phase 1 proposal we will optimize the 3Plex, a miniaturized point- of-care lateral flow test for finger prick blood or serum that can distinguish between infections of DENV, ZIKV, and CHIKV, and integrate our patented phone-based image recognition application with geo-referenced applications (e.g. District Health Information System 2, DHIS2) for real-time epidemiological reporting. Our proposed 3Plex is a low-cost test that can be done in the field without moving parts, or the necessity of trained personnel, expensive equipment, or a centralized laboratory. Studies proposed here will leverage our recent advances, including identification of monoclonal antibodies (mAbs) that recognize flaviviral non-structural protein 1 (NS1)- or alphaviral envelope (E)-specific antigen with optimal binding capacity and without cross-reactivity when utilized in a single, multiplex test. The intellectual property of the diagnostic technology being optimized in this SBIR was developed by the investigators and global rights are exclusively held by E25Bio. In Aim 1, we will fine tune a panel of immunological characterization assays focusing on mAb specificity and sensitivity and scale up production of candidate monoclonal antibodies. In Aim 2, we will advance development and validation of the 3Plex prototype. We will test DENV, ZIKV, and CHIKV supernatants from infected Vero cells, and confirm the limit of detection with de-identified human samples. In Aim 3, we will integrate our mobile phone image recognition application with predictive algorithms to report data in real-time. This project is innovative because it is the first platform that allows point-of-care arboviral antigen-based detection and real-time data analytics for infection geotagging and outbreak management. Upon successful completion of this Phase I project, we will have developed a first-of-its-kind platform, which provides an all-in-one solution to arboviral detection and data reporting, that will be ready for field testing and regulatory validation. These results will lead to a Phase II project to conduct clinical trials in Colombia to assess diagnostic efficacy in clinical settings.